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DataFrameStatFunctions.scala
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DataFrameStatFunctions.scala
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql
import org.apache.spark.annotation.Experimental
import org.apache.spark.sql.execution.stat._
/**
* :: Experimental ::
* Statistic functions for [[DataFrame]]s.
*/
@Experimental
final class DataFrameStatFunctions private[sql](df: DataFrame) {
/**
* Finding frequent items for columns, possibly with false positives. Using the
* frequent element count algorithm described in
* [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]].
* The `support` should be greater than 1e-4.
*
* @param cols the names of the columns to search frequent items in.
* @param support The minimum frequency for an item to be considered `frequent`. Should be greater
* than 1e-4.
* @return A Local DataFrame with the Array of frequent items for each column.
*/
def freqItems(cols: Array[String], support: Double): DataFrame = {
FrequentItems.singlePassFreqItems(df, cols, support)
}
/**
* Runs `freqItems` with a default `support` of 1%.
*
* @param cols the names of the columns to search frequent items in.
* @return A Local DataFrame with the Array of frequent items for each column.
*/
def freqItems(cols: Array[String]): DataFrame = {
FrequentItems.singlePassFreqItems(df, cols, 0.01)
}
/**
* Python friendly implementation for `freqItems`
*/
def freqItems(cols: List[String], support: Double): DataFrame = {
FrequentItems.singlePassFreqItems(df, cols, support)
}
/**
* Python friendly implementation for `freqItems` with a default `support` of 1%.
*/
def freqItems(cols: List[String]): DataFrame = {
FrequentItems.singlePassFreqItems(df, cols, 0.01)
}
/**
* Calculate the covariance of two numerical columns of a DataFrame.
* @param col1 the name of the first column
* @param col2 the name of the second column
* @return the covariance of the two columns.
*/
def cov(col1: String, col2: String): Double = {
StatFunctions.calculateCov(df, Seq(col1, col2))
}
}